Crowd Counting Using Deep Learning Techniques

Abstract

Due to rapid growth of the World population, urbanization generates crowding situations which pose challenges to public safety, security and to the management of crowd. Manual analysis of crowded situations is a tedious job and usually prone to errors. Traditional handcrafted crowd-counting techniques in an image are currently transformed via machine-learning and artificial-intelligence techniques into intelligent crowd-counting techniques. This paradigm shift offers many advanced features in terms of adaptive monitoring and the control of dynamic crowd gatherings. Adaptive moni-toring, identification/recognition, and the management of diverse crowd gatherings can improve many crowd-managenent-related tasks in terms of efficiency, capacity. relia-bility, and safery.